{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Box-Cox transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# for plotting\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# for Q-Q plots\n",
    "import scipy.stats as stats\n",
    "\n",
    "# the dataset for the demo\n",
    "from sklearn.datasets import fetch_california_housing\n",
    "\n",
    "# with open-source packages\n",
    "from sklearn.preprocessing import PowerTransformer\n",
    "from feature_engine.transformation import BoxCoxTransformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.3252</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>1.023810</td>\n",
       "      <td>322.0</td>\n",
       "      <td>2.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.3014</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.971880</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>2.109842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.2574</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>1.073446</td>\n",
       "      <td>496.0</td>\n",
       "      <td>2.802260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.6431</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>1.073059</td>\n",
       "      <td>558.0</td>\n",
       "      <td>2.547945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.8462</td>\n",
       "      <td>52.0</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>1.081081</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.181467</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup\n",
       "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556\n",
       "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842\n",
       "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260\n",
       "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945\n",
       "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the California House price data from Scikit-learn\n",
    "X, y = fetch_california_housing(return_X_y=True, as_frame=True)\n",
    "\n",
    "# drop lat and lon\n",
    "X.drop(labels=[\"Latitude\", \"Longitude\"], axis=1, inplace=True)\n",
    "\n",
    "# display top 5 rows\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams[\"figure.dpi\"] = 450"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot histograms to inspect variable distributions\n",
    "\n",
    "X.hist(bins=30, figsize=(12, 12), layout=(3, 3))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# capture variable names in a list\n",
    "\n",
    "variables = list(X.columns)\n",
    "\n",
    "variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make Q-Q plots for all variables\n",
    "\n",
    "\n",
    "def make_qqplot(df):\n",
    "\n",
    "    plt.figure(figsize=(10, 6), constrained_layout=True)\n",
    "\n",
    "    for i in range(6):\n",
    "\n",
    "        # location in figure\n",
    "        ax = plt.subplot(2, 3, i + 1)\n",
    "\n",
    "        # variable to plot\n",
    "        var = variables[i]\n",
    "\n",
    "        # q-q plot\n",
    "        stats.probplot((df[var]), dist=\"norm\", plot=plt)\n",
    "\n",
    "        # add variable name as title\n",
    "        ax.set_title(var)\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "make_qqplot(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Box-Cox transformation with Scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize the transformer with box-cox\n",
    "\n",
    "transformer = PowerTransformer(\n",
    "    method=\"box-cox\", standardize=False).set_output(transform=\"pandas\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PowerTransformer(method=&#x27;box-cox&#x27;, standardize=False)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;PowerTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.PowerTransformer.html\">?<span>Documentation for PowerTransformer</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>PowerTransformer(method=&#x27;box-cox&#x27;, standardize=False)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "PowerTransformer(method='box-cox', standardize=False)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit transformer: transformer will learn the lambdas\n",
    "\n",
    "transformer.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.09085447,  0.80939807, -0.2980049 , -1.62900029,  0.23576757,\n",
       "       -0.47630324])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lambdas are stored in a transformer attribute\n",
    "\n",
    "transformer.lambdas_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.337069</td>\n",
       "      <td>23.723215</td>\n",
       "      <td>1.475350</td>\n",
       "      <td>0.023085</td>\n",
       "      <td>12.308116</td>\n",
       "      <td>0.756645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.333598</td>\n",
       "      <td>13.286954</td>\n",
       "      <td>1.410978</td>\n",
       "      <td>-0.029195</td>\n",
       "      <td>22.335340</td>\n",
       "      <td>0.628291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.171690</td>\n",
       "      <td>29.017457</td>\n",
       "      <td>1.568866</td>\n",
       "      <td>0.066936</td>\n",
       "      <td>14.082653</td>\n",
       "      <td>0.814315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.873879</td>\n",
       "      <td>29.017457</td>\n",
       "      <td>1.370082</td>\n",
       "      <td>0.066615</td>\n",
       "      <td>14.598634</td>\n",
       "      <td>0.754737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.432988</td>\n",
       "      <td>29.017457</td>\n",
       "      <td>1.415020</td>\n",
       "      <td>0.073214</td>\n",
       "      <td>14.654092</td>\n",
       "      <td>0.651500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     MedInc   HouseAge  AveRooms  AveBedrms  Population  AveOccup\n",
       "0  2.337069  23.723215  1.475350   0.023085   12.308116  0.756645\n",
       "1  2.333598  13.286954  1.410978  -0.029195   22.335340  0.628291\n",
       "2  2.171690  29.017457  1.568866   0.066936   14.082653  0.814315\n",
       "3  1.873879  29.017457  1.370082   0.066615   14.598634  0.754737\n",
       "4  1.432988  29.017457  1.415020   0.073214   14.654092  0.651500"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# transform data: returns NumPy array\n",
    "\n",
    "X_tf = transformer.transform(X)\n",
    "\n",
    "X_tf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot histograms to inspect variable distributions\n",
    "\n",
    "X_tf.hist(bins=30, figsize=(12, 12), layout=(3, 3))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make Q-Q plots for all variables\n",
    "\n",
    "make_qqplot(X_tf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Box-Cox transformation with Feature-engine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>BoxCoxTransformer()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;BoxCoxTransformer<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>BoxCoxTransformer()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "BoxCoxTransformer()"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set up the transformer: automatically identifies numerical variables\n",
    "\n",
    "bct = BoxCoxTransformer()\n",
    "\n",
    "# fit transformer to the data set\n",
    "bct.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'MedInc': 0.0908544707230882,\n",
       " 'HouseAge': 0.8093980691990604,\n",
       " 'AveRooms': -0.2980049020502605,\n",
       " 'AveBedrms': -1.629000289975924,\n",
       " 'Population': 0.23576756814719804,\n",
       " 'AveOccup': -0.4763032366480272}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the exponents for each variable\n",
    "\n",
    "bct.lambda_dict_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transform variables: returns a new dataframe\n",
    "\n",
    "X_tf = bct.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot histograms to inspect variable distributions\n",
    "\n",
    "X_tf.hist(bins=30, figsize=(12, 12), layout=(3, 3))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make Q-Q plots for all variables\n",
    "\n",
    "make_qqplot(X_tf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Box-Cox transformation with SciPy\n",
    "\n",
    "One variable at a time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make a copy of the dataframe where we will store the modified\n",
    "# variables\n",
    "\n",
    "X_tf = X.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal λ:  0.0908544707230882\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# apply the Box-Cox transformation to variable MedInc\n",
    "X_tf[\"MedInc\"], param = stats.boxcox(X[\"MedInc\"])\n",
    "\n",
    "# print the optimal lambda found for MedInc\n",
    "print(\"Optimal λ: \", param)\n",
    "\n",
    "# visualize the transformed variable\n",
    "X_tf[\"MedInc\"].hist(bins=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "fsml",
   "language": "python",
   "name": "fsml"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "921.556px",
    "left": "0px",
    "right": "1744px",
    "top": "110.444px",
    "width": "226.333px"
   },
   "toc_section_display": "block",
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
